Named Entity Recognition andSemantic Relation Extractionfrom Cochrane Review Documents.

Motivation & Approach

To make Cochrane contents – evidence - more accessible

Cross referencing information

finding relevant passages in documents of 200+ pages

Supporting discovery & search in Cochrane review documents

Build a foundation for apps to be used in „point of care“ situations

Extract an ontology based on information contained in the Cochrane library

„discover“ Cochrane content relevant to a given patient

Using semantic models (IBM‘s System T) to extract entities &

relations

diseases, diagnoses, treatments, interventions, medication, drugs,

symptoms, complications

„… prolonged treatment with vitamin K antagonists reduces the risk of

recurrent venous thromboembolism …. ”

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L. Chiticariu, R. Krishnamurthy, Y. Li, F. Reiss, and S. Vaithyanathan, “Domain adaptation of rule-based annotators for named-entity recognition tasks,” in

Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 2010, pp. 1002–1012.

A. Nagesh, G. Ramakrishnan, L. Chiticariu, R. Krishnamurthy, A. Dharkar, and P. Bhattacharyya, “Towards efficient named-entity rule induction for customizability,”

in Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2012, pp.

128–138.

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Semantic models using System T / AQL

•Extractcandidatefeatures

•Apply filters

•Post processing

Dictionary

Learning

•Extract basicfeatures

•Combine features

•Annotate andcanonical form

Named Entity

Recognition

•Combine modifiers withentities

•Combine Relation Hintswith extendedspans

•Normalize

Relation Identification

Named Entity Recognition / Build Dictionaries

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Extract Candidate Features – Context Hints

:treatment <is> <more> effective than :treatment

/(administer|apply)ing/ :treatment

/tak(e|ing)/ :drug

:drug <consumption>

/doses used in/ :disease

/(health )?consequences? of/ :disease

/(medication|therapy|treatment) for/ :disease

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Dictionary

Learning

Dictionary Learning - Postprocessing

Shorten by intrinsic hierarchy

„long-term compression therapy“ „compression therapy“

“probable ischaemic stroke“ „ischaemic stroke“

(use match statistics as heuristic measures)

Filter using statistics

remove „exploded terms“(few original matches, many dictionary matches)

remove „weak terms“(few matches, but many matches for parent)

Type Deduction

Type candidates from source extractor modules

„same list“ (entities mentioned within a list)

comparation patterns: „compare with“, „in combination with“

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Dictionary

Learning

Semantic Relation Extraction

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Building Blocks for Relation Extraction

Named Entities

(„calcium channel blockers“, „parkinson‘s

disease“)

Relation Hints

(A was caused by B; A has positive effects on B …)

tag with relation type: CAUSE, PREVENT,

INCREASE, …

Modifier Hints

(use of A; risk of A; developing A …)

tag with modifier type: RISK, USE, INFECTION,

GROWTH, …Page 8

Relation Identification

Illustrated cases

List and Bracket Processing

„Other drugs include carbamazepine and newer antiepileptics

(lamotrigine, topiramate and zonisamide) and the atypical

antipsychotics (clozapine, aripiprazole and ziprasidone)“

Special Cases (for “is a”)

„atypical antipsychotics (clozapine, aripiprazole and ziprasidone)“

„infections, such as malaria and hookworm”

„selenium, vitamin C and other antioxidants“

Simple direct relations

@PREVENT :entity /(protect|help)s against/ :entity

@PREVENT :entity <can> <adverb> ? prevent :entity

@CAUSE :entity <is> <adverb>? followed by :entity

@CAUSE :entity <can> <adverb>? result in :entity

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Relation Identification

Relation Postprocessing

Combine consecutive modifiers and Named Entities:„a reduction in the risk of developing A“

REDUCE.RISK.GROWTH

Combine Relation Hints and Extended Entity Spans:„use of calcium channel blockers was associated with a reduction in the risk of developing

parkinson‘s disease“

USE A CAUSE REDUCE.RISK.GROWTH B

Simplify („translate“) to create the final Semantic Relation

A REDUCE RISK B

calcium channel blockers REDUCE RISK parkinson‘s

disease

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Relation Identification

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Cochrane Ontology Viewer

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Text passage supporting a relation

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Document context of the text passage

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Cochrane Ontology Viewer – other relations in Chen …

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Effectiveness of drugs …

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Evidence for drugs reducing risk of thrombosis

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What else do we know about „ethynilestradiol? “

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Relations found in several documents

Observations and insights gained …

Rule based system adequate for medical reports

Statistical approaches require larger corpora

Grammatical parsers alone not sufficiently specific

Domain specific language aids semantic modelling

Problems encountered, responses (POS)

Adjective contamination

Some antiepileptic drugs are marketed specifically for migraine prophylaxis.

Delimiting entities and relations

Drug therapy for migraine falls into two categories.

Patients were likely to reduce the number of their migraine headaches by 50%.

Efforts commensurate with the text corpus

Continuous improvement process inherent in our approach

Building on top of the existing dictionaries and patterns

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What‘s next?

Improve AQL extraction results

Improve entity normalization and types

(eg. make better use of entity components: „endothelin receptor

antagonist“)

Identify most relevant relations

Extraction of Structured Context

Use ontology for point of care situations

Introduce deep learning technology

User interface (mobile systems of engagement) for „point of care“

situations

Combine with patient data to guide the discovery process in

Cochrane reviews

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